activity
20192024
most citedFederated Learning of User Authentication Models

5 citations · 12 across the 6 of their papers we have counts for

collaborators

8 papers

cs.LG2021

Federated Learning of User Verification Models Without Sharing Embeddings

Hossein Hosseini, Hyunsin Park, Sungrack Yun +3

We consider the problem of training User Verification (UV) models in federated setting, where each user has access to the data of only one class and user embeddings cannot be share…

cs.LG20212 cited

Prototype-based Personalized Pruning

Jangho Kim, Simyung Chang, Sungrack Yun +1

Nowadays, as edge devices such as smartphones become prevalent, there are increasing demands for personalized services. However, traditional personalization methods are not suitabl…

cs.SD20211 cited

SubSpectral Normalization for Neural Audio Data Processing

Simyung Chang, Hyoungwoo Park, Janghoon Cho +3

Convolutional Neural Networks are widely used in various machine learning domains. In image processing, the features can be obtained by applying 2D convolution to all spatial dimen…

cs.LG20205 cited

Federated Learning of User Authentication Models

Hossein Hosseini, Sungrack Yun, Hyunsin Park +3

Machine learning-based User Authentication (UA) models have been widely deployed in smart devices. UA models are trained to map input data of different users to highly separable em…

cs.CV2020

End-to-End Lane Marker Detection via Row-wise Classification

Seungwoo Yoo, Heeseok Lee, Heesoo Myeong +4

In autonomous driving, detecting reliable and accurate lane marker positions is a crucial yet challenging task. The conventional approaches for the lane marker detection problem pe…

cs.SD20193 cited

Weakly Labeled Sound Event Detection Using Tri-training and Adversarial Learning

Hyoungwoo Park, Sungrack Yun, Jungyun Eum +2

This paper considers a semi-supervised learning framework for weakly labeled polyphonic sound event detection problems for the DCASE 2019 challenge's task4 by combining both the tr…